Modeling Bus Passenger Flow Dynamics Using the Cell Transmission Model for Real‐Time Congestion Management
Bibliographic record
Abstract
This research develops the BUS‐CTM, a novel mathematical simulation model that adapts the cell transmission model (CTM) to analyze spatiotemporal passenger flow dynamics in urban bus networks. The framework discretizes bus routes into interconnected cells bounded by adjacent stops, enabling simultaneous tracking of passenger density evolution and bus traffic interactions through a unified state‐space representation. By integrating real‐time data streams—including GPS trajectories, automatic passenger counters (APCs) records, and VISSIM‐simulated traffic dynamics—the model captures critical nonlinearities in boarding/alighting processes and network‐wide congestion propagation at shared stops. Numerical experiments on Gainesville’s RTS network demonstrate the model’s accuracy in predicting passenger distributions, achieving a 4% mean absolute percentage error (MAPE) during peak hours (6:30–9:45 a.m.) and successfully identifying bottlenecks where densities exceed 85% of capacity. The BUS‐CTM advances prior CTM adaptations through three key innovations: (1) integration of mixed‐traffic capacity reduction effects to account for bus‐induced roadway bottlenecks, (2) modular parameterization for transferability across diverse transit systems, and (3) real‐time applicability via embedded calibration protocols for door throughput ( C door = 1.2 pax/s) and fare efficiency ( γ = 0.8–1.0). These contributions provide transit agencies with a computationally efficient tool for optimizing service frequency, mitigating crowding, and improving network resilience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".